A comparison of the hazard perception ability of matched groups of healthy drivers aged 35 to 55, 65 to 74, and 75 to 84 years

We examined differences in response latencies obtained during a validated video-based hazard perception driving test between three healthy, community-dwelling groups: 22 mid-aged (35-55 years), 34 young-old (65-74 years), and 23 old-old (75-84 years) current drivers, matched for gender, education level, and vocabulary. We found no significant difference in performance between mid-aged and young-old groups, but the old-old group was significantly slower than the other two groups. The differences between the old-old group and the other groups combined were independently mediated by useful field of view (UFOV), contrast sensitivity, and simple reaction time measures. Given that hazard perception latency has been linked with increased crash risk, these results are consistent with the idea that increased crash risk in older adults could be a function of poorer hazard perception, though this decline does not appear to manifest until age 75+ in healthy drivers.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

The rate-limiting mechanism for the heterogeneous burning of cylindrical iron rods

This paper presents the findings of an investigation into the rate-limiting mechanism for the heterogeneous burning in oxygen under normal gravity and microgravity of cylindrical iron rods. The original objective of the work was to determine why the observed melting rate for burning 3.2-mm diameter iron rods is significantly higher in microgravity than in normal gravity. This work, however, also provided fundamental insight into the rate-limiting mechanism for heterogeneous burning. The paper includes a summary of normal-gravity and microgravity experimental results, heat transfer analysis and post-test microanalysis of quenched samples. These results are then used to show that heat transfer across the solid/liquid interface is the rate-limiting mechanism for melting and burning, limited by the interfacial surface area between the molten drop and solid rod. In normal gravity, the work improves the understanding of trends reported during standard flammability testing for metallic materials, such as variations in melting rates between test specimens with the same cross-sectional area but different crosssectional shape. The work also provides insight into the effects of configuration and orientation, leading to an improved application of standard test results in the design of oxygen system components. For microgravity applications, the work enables the development of improved methods for lower cost metallic material flammability testing programs. In these ways, the work provides fundamental insight into the heterogeneous burning process and contributes to improved fire safety for oxygen systems in applications involving both normal-gravity and microgravity environments.

A proposed qualitative framework for heterogeneous burning of metallic materials : the 'melting rate triangle'

This paper presents a proposed qualitative framework to discuss the heterogeneous burning of metallic materials, through parameters and factors that influence the melting rate of the solid metallic fuel (either in a standard test or in service). During burning, the melting rate is related to the burning rate and is therefore an important parameter for describing and understanding the burning process, especially since the melting rate is commonly recorded during standard flammability testing for metallic materials and is incorporated into many relative flammability ranking schemes. However, whilst the factors that influence melting rate (such as oxygen pressure or specimen diameter) have been well characterized, there is a need for an improved understanding of how these parameters interact as part of the overall melting and burning of the system. Proposed here is the ‘Melting Rate Triangle’, which aims to provide this focus through a conceptual framework for understanding how the melting rate (of solid fuel) is determined and regulated during heterogeneous burning. In the paper, the proposed conceptual model is shown to be both (a) consistent with known trends and previously observed results, and (b)capable of being expanded to incorporate new data. Also shown are examples of how the Melting Rate Triangle can improve the interpretation of flammability test results. Slusser and Miller previously published an ‘Extended Fire Triangle’ as a useful conceptual model of ignition and the factors affecting ignition, providing industry with a framework for discussion. In this paper it is shown that a ‘Melting Rate Triangle’ provides a similar qualitative framework for burning, leading to an improved understanding of the factors affecting fire propagation and extinguishment.

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Poster abstract : Energy-efficient rate adaptive MAC Protocol (RA-MAC) for long-lived sensor networks

This paper introduces an energy-efficient Rate Adaptive MAC (RA-MAC) protocol for long-lived Wireless Sensor Networks (WSN). Previous research shows that the dynamic and lossy nature of wireless communication is one of the major challenges to reliable data delivery in a WSN. RA-MAC achieves high link reliability in such situations by dynamically trading off radio bit rate for signal processing gain. This extra gain reduces the packet loss rate which results in lower energy expenditure by reducing the number of retransmissions. RA-MAC selects the optimal data rate based on channel conditions with the aim of minimizing energy consumption. We have implemented RA-MAC in TinyOS on an off-the-shelf sensor platform (TinyNode), and evaluated its performance by comparing RA-MAC with state-ofthe- art WSN MAC protocol (SCP-MAC) by experiments.

Use of IP addresses for high rate flooding attack detection

High-rate flooding attacks (aka Distributed Denial of Service or DDoS attacks) continue to constitute a pernicious threat within the Internet domain. In this work we demonstrate how using packet source IP addresses coupled with a change-point analysis of the rate of arrival of new IP addresses may be sufficient to detect the onset of a high-rate flooding attack. Importantly, minimizing the number of features to be examined, directly addresses the issue of scalability of the detection process to higher network speeds. Using a proof of concept implementation we have shown how pre-onset IP addresses can be efficiently represented using a bit vector and used to modify a “white list” filter in a firewall as part of the mitigation strategy.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Safety-restraint use rate as function of law enforcement and other factors : preliminary analysis

Persistent use of safety restraints prevents deaths and reduces the severity and number of injuries resulting from motor vehicle crashes. However, safety-restraint use rates in the United States have been below those of other nations with safety-restraint enforcement laws. With a better understanding of the relationship between safety-restraint law enforcement and safety-restraint use, programs can be implemented to decrease the number of deaths and injuries resulting from motor vehicle crashes. Does safety-restraint use increase as enforcement increases? Do motorists increase their safety-restraint use in response to the general presence of law enforcement or to targeted law enforcement efforts? Does a relationship between enforcement and restraint use exist at the countywide level? A logistic regression model was estimated by using county-level safety-restraint use data and traffic citation statistics collected in 13 counties within the state of Florida in 1997. The model results suggest that safety-restraint use is positively correlated with enforcement intensity, is negatively correlated with safety-restraint enforcement coverage (in lanemiles of enforcement coverage), and is greater in urban than rural areas. The quantification of these relationships may assist Florida and other law enforcement agencies in raising safety-restraint use rates by allocating limited funds more efficiently either by allocating additional time for enforcement activities of the existing force or by increasing enforcement staff. In addition, the research supports a commonsense notion that enforcement activities do result in behavioral response.

Nitrogen fertilizer rate management as a nitrous oxide mitigation strategy: Development of a nitrous oxide emission reduction protocol (NERP)

Nitrous oxide (N2O) is a potent agricultural greenhouse gas (GHG). More than 50% of the global anthropogenic N2O flux is attributable to emissions from soil, primarily due to large fertilizer nitrogen (N) applications to corn and other non-leguminous crops. Quantification of the trade–offs between N2O emissions, fertilizer N rate, and crop yield is an essential requirement for informing management strategies aiming to reduce the agricultural sector GHG burden, without compromising productivity and producer livelihood. There is currently great interest in developing and implementing agricultural GHG reduction offset projects for inclusion within carbon offset markets. Nitrous oxide, with a global warming potential (GWP) of 298, is a major target for these endeavours due to the high payback associated with its emission prevention. In this paper we use robust quantitative relationships between fertilizer N rate and N2O emissions, along with a recently developed approach for determining economically profitable N rates for optimized crop yield, to propose a simple, transparent, and robust N2O emission reduction protocol (NERP) for generating agricultural GHG emission reduction credits. This NERP has the advantage of providing an economic and environmental incentive for producers and other stakeholders, necessary requirements in the implementation of agricultural offset projects.

Indentation size effect and strain rate sensitivity of nanocrystalline Mg-Al alloys

Deformation Behaviour of microcrystalline (mc) and nanocrystalline (nc) Mg-5%Al alloys produced by hot extrusion of ball-milled powders were investigated using instrumented indentation tests. The hardness values of the mc and nc metals exhibited indentation size effect (ISE), with nc alloys showing weaker ISE. The highly localized dislocation activities resulted in a small activation volume, hence enhanced strain rate sensitivity. Relative higher strain rate sensitivity and the negative Hall-Petch Relationship suggested the increasingly important role of grain boundary mediated mechanisms when the grain size decreased to nanometer region.

Reliability prediction using the non-parametric explicit hazard model : a case study

Survival probability prediction using covariate-based hazard approach is a known statistical methodology in engineering asset health management. We have previously reported the semi-parametric Explicit Hazard Model (EHM) which incorporates three types of information: population characteristics; condition indicators; and operating environment indicators for hazard prediction. This model assumes the baseline hazard has the form of the Weibull distribution. To avoid this assumption, this paper presents the non-parametric EHM which is a distribution-free covariate-based hazard model. In this paper, an application of the non-parametric EHM is demonstrated via a case study. In this case study, survival probabilities of a set of resistance elements using the non-parametric EHM are compared with the Weibull proportional hazard model and traditional Weibull model. The results show that the non-parametric EHM can effectively predict asset life using the condition indicator, operating environment indicator, and failure history.